GoogleNet代码实现

下面是GoogleNet的PyTorch实现代码：

import torch
import torch.nn as nn
import torch.nn.functional as F

class Inception(nn.Module):
    def __init__(self, in_channels, out1x1, reduce3x3, out3x3, reduce5x5, out5x5, pool):
        super(Inception, self).__init__()
        # 1x1 conv branch
        self.branch1 = nn.Conv2d(in_channels, out1x1, kernel_size=1)

        # 3x3 conv branch
        self.branch2 = nn.Sequential(
            nn.Conv2d(in_channels, reduce3x3, kernel_size=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(reduce3x3, out3x3, kernel_size=3, padding=1)
        )

        # 5x5 conv branch
        self.branch3 = nn.Sequential(
            nn.Conv2d(in_channels, reduce5x5, kernel_size=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(reduce5x5, out5x5, kernel_size=5, padding=2)
        )

        # maxpool branch
        self.branch4 = nn.Sequential(
            nn.MaxPool2d(kernel_size=3, stride=1, padding=1),
            nn.Conv2d(in_channels, pool, kernel_size=1)
        )

    def forward(self, x):
        branch1 = self.branch1(x)
        branch2 = self.branch2(x)
        branch3 = self.branch3(x)
        branch4 = self.branch4(x)
        outputs = [branch1, branch2, branch3, branch4]
        return torch.cat(outputs, 1)

class GoogleNet(nn.Module):
    def __init__(self, num_classes=1000):
        super(GoogleNet, self).__init__()

        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3)
        self.pool1 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)

        self.conv2_reduce = nn.Conv2d(64, 64, kernel_size=1)
        self.conv2 = nn.Conv2d(64, 192, kernel_size=3, stride=1, padding=1)
        self.pool2 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)

        self.inception3a = Inception(192, 64, 96, 128, 16, 32, 32)
        self.inception3b = Inception(256, 128, 128, 192, 32, 96, 64)
        self.pool3 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)

        self.inception4a = Inception(480, 192, 96, 208, 16, 48, 64)
        self.inception4b = Inception(512, 160, 112, 224, 24, 64, 64)
        self.inception4c = Inception(512, 128, 128, 256, 24, 64, 64)
        self.inception4d = Inception(512, 112, 144, 288, 32, 64, 64)
        self.inception4e = Inception(528, 256, 160, 320, 32, 128, 128)
        self.pool4 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)

        self.inception5a = Inception(832, 256, 160, 320, 32, 128, 128)
        self.inception5b = Inception(832, 384, 192, 384, 48, 128, 128)

        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.dropout = nn.Dropout2d(p=0.4)
        self.fc = nn.Linear(1024, num_classes)

    def forward(self, x):
        x = self.pool1(F.relu(self.conv1(x)))
        x = self.pool2(F.relu(self.conv2_reduce(x)))
        x = self.inception3a(x)
        x = self.inception3b(x)
        x = self.pool3(x)
        x = self.inception4a(x)
        x = self.inception4b(x)
        x = self.inception4c(x)
        x = self.inception4d(x)
        x = self.inception4e(x)
        x = self.pool4(x)
        x = self.inception5a(x)
        x = self.inception5b(x)
        x = self.avgpool(x)
        x = self.dropout(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)
        return x

这里实现了GoogleNet的基本结构，包括Inception模块和整个网络的前向计算。在实现中使用了PyTorch框架提供的模块化接口，代码结构相对清晰，并且方便调用。